DESCRIPTION: (Applicant's Abstract) The overall objective of this project is to develop a genetic staging system for early breast cancer that will permit the identification of patients with early disease who are at high risk for recurrence, and who are most likely to benefit from standard adjuvant therapy or from intensive treatment. The basic principles that underlie this application are that within each breast cancer, tumor cells accumulate genetic abnormalities as they evolve to a more aggressive malignancy, and that at some point in their genetic evolution some cells accumulate the critical genetic abnormalities that enable them to metastasize. If one could perform appropriate measurements on tumor cells obtained from a given patient's breast cancer at the time of surgery, and determine if the tumor contained cells that had the requisite genetic changes for the acquisition of the capacity to metastasize, one could use this information directly to develop an individualized clinical treatment plan for that patient. The applicant's studies and those of others have suggested that there at least three major genetic evolutionary pathways in human solid tumors, including breast cancer. These are the microsatellite instability pathway, and two pathways that depend on the genetic instability that results from the loss of wild type p53, namely, the adenosquamous genetic evolutionary pathway and the neuroendocrine genetic evolutionary pathway. The applicant's work to date has focused on the elucidation of the genetic sequences of the adenosquamous pathway. Using multiparameter flow cytometric and multiparameter FISH techniques, he has established that the sequence, p53 loss followed by Her-2/neu overexpression + EGF receptor overexpression followed by ras overexpression, occurs in the majority of human breast cancers. He now proposes, a) to elucidate the genetic evolutionary changes that lie downstream of ras overexpression in breast cancer, b) to elucidate the neuroendocrine genetic evolutionary pathway in breast cancer, c) to optimize the strategy for combining multiparameter flow cytometric measurements and FISH studies in the characterization of the genetic evolutionary pathways in clinical breast tumor samples, d) to develop and test new flow cytometric measurements and new combinations of multiparameter measurements for the characterization of genetic evolutionary changes in clinical breast cancer samples, and e) to develop statistical modeling approaches to extract genetic evolutionary sequencing information from the increasingly more complex data sets being collected in his multiparameter studies.